Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations506
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory55.5 KiB
Average record size in memory112.3 B

Variable types

Numeric13
Categorical1

Alerts

AGE is highly overall correlated with CRIM and 7 other fieldsHigh correlation
CRIM is highly overall correlated with AGE and 8 other fieldsHigh correlation
DIS is highly overall correlated with AGE and 6 other fieldsHigh correlation
INDUS is highly overall correlated with AGE and 7 other fieldsHigh correlation
LSTAT is highly overall correlated with AGE and 7 other fieldsHigh correlation
NOX is highly overall correlated with AGE and 8 other fieldsHigh correlation
PRICE is highly overall correlated with AGE and 7 other fieldsHigh correlation
PTRATIO is highly overall correlated with PRICEHigh correlation
RAD is highly overall correlated with CRIM and 2 other fieldsHigh correlation
RM is highly overall correlated with LSTAT and 1 other fieldsHigh correlation
TAX is highly overall correlated with AGE and 7 other fieldsHigh correlation
ZN is highly overall correlated with AGE and 4 other fieldsHigh correlation
CHAS is highly imbalanced (63.7%) Imbalance
ZN has 372 (73.5%) zeros Zeros

Reproduction

Analysis started2025-07-16 06:44:52.253769
Analysis finished2025-07-16 06:45:18.688381
Duration26.43 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

CRIM
Real number (ℝ)

High correlation 

Distinct504
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6135236
Minimum0.00632
Maximum88.9762
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-07-16T11:45:18.755705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.00632
5-th percentile0.02791
Q10.082045
median0.25651
Q33.6770825
95-th percentile15.78915
Maximum88.9762
Range88.96988
Interquartile range (IQR)3.5950375

Descriptive statistics

Standard deviation8.6015451
Coefficient of variation (CV)2.3803761
Kurtosis37.130509
Mean3.6135236
Median Absolute Deviation (MAD)0.22145
Skewness5.2231488
Sum1828.4429
Variance73.986578
MonotonicityNot monotonic
2025-07-16T11:45:18.850106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01501 2
 
0.4%
14.3337 2
 
0.4%
0.00632 1
 
0.2%
0.02731 1
 
0.2%
0.02729 1
 
0.2%
0.03237 1
 
0.2%
0.06905 1
 
0.2%
0.02985 1
 
0.2%
0.20746 1
 
0.2%
8.05579 1
 
0.2%
Other values (494) 494
97.6%
ValueCountFrequency (%)
0.00632 1
0.2%
0.00906 1
0.2%
0.01096 1
0.2%
0.01301 1
0.2%
0.01311 1
0.2%
0.0136 1
0.2%
0.01381 1
0.2%
0.01432 1
0.2%
0.01439 1
0.2%
0.01501 2
0.4%
ValueCountFrequency (%)
88.9762 1
0.2%
73.5341 1
0.2%
67.9208 1
0.2%
51.1358 1
0.2%
45.7461 1
0.2%
41.5292 1
0.2%
38.3518 1
0.2%
37.6619 1
0.2%
28.6558 1
0.2%
25.9406 1
0.2%

ZN
Real number (ℝ)

High correlation  Zeros 

Distinct26
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.363636
Minimum0
Maximum100
Zeros372
Zeros (%)73.5%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-07-16T11:45:18.913665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312.5
95-th percentile80
Maximum100
Range100
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation23.322453
Coefficient of variation (CV)2.0523759
Kurtosis4.0315101
Mean11.363636
Median Absolute Deviation (MAD)0
Skewness2.2256663
Sum5750
Variance543.93681
MonotonicityNot monotonic
2025-07-16T11:45:18.988904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 372
73.5%
20 21
 
4.2%
80 15
 
3.0%
22 10
 
2.0%
25 10
 
2.0%
12.5 10
 
2.0%
40 7
 
1.4%
30 6
 
1.2%
45 6
 
1.2%
90 5
 
1.0%
Other values (16) 44
 
8.7%
ValueCountFrequency (%)
0 372
73.5%
12.5 10
 
2.0%
17.5 1
 
0.2%
18 1
 
0.2%
20 21
 
4.2%
21 4
 
0.8%
22 10
 
2.0%
25 10
 
2.0%
28 3
 
0.6%
30 6
 
1.2%
ValueCountFrequency (%)
100 1
 
0.2%
95 4
 
0.8%
90 5
 
1.0%
85 2
 
0.4%
82.5 2
 
0.4%
80 15
3.0%
75 3
 
0.6%
70 3
 
0.6%
60 4
 
0.8%
55 3
 
0.6%

INDUS
Real number (ℝ)

High correlation 

Distinct76
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.136779
Minimum0.46
Maximum27.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-07-16T11:45:19.071939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.46
5-th percentile2.18
Q15.19
median9.69
Q318.1
95-th percentile21.89
Maximum27.74
Range27.28
Interquartile range (IQR)12.91

Descriptive statistics

Standard deviation6.8603529
Coefficient of variation (CV)0.61600874
Kurtosis-1.2335396
Mean11.136779
Median Absolute Deviation (MAD)6.32
Skewness0.29502157
Sum5635.21
Variance47.064442
MonotonicityNot monotonic
2025-07-16T11:45:19.152494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.1 132
26.1%
19.58 30
 
5.9%
8.14 22
 
4.3%
6.2 18
 
3.6%
21.89 15
 
3.0%
3.97 12
 
2.4%
9.9 12
 
2.4%
10.59 11
 
2.2%
8.56 11
 
2.2%
5.86 10
 
2.0%
Other values (66) 233
46.0%
ValueCountFrequency (%)
0.46 1
 
0.2%
0.74 1
 
0.2%
1.21 1
 
0.2%
1.22 1
 
0.2%
1.25 2
0.4%
1.32 1
 
0.2%
1.38 1
 
0.2%
1.47 2
0.4%
1.52 4
0.8%
1.69 2
0.4%
ValueCountFrequency (%)
27.74 5
 
1.0%
25.65 7
 
1.4%
21.89 15
 
3.0%
19.58 30
 
5.9%
18.1 132
26.1%
15.04 3
 
0.6%
13.92 5
 
1.0%
13.89 4
 
0.8%
12.83 6
 
1.2%
11.93 5
 
1.0%

CHAS
Categorical

Imbalance 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size24.8 KiB
0
471 
1
 
35

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters506
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 471
93.1%
1 35
 
6.9%

Length

2025-07-16T11:45:19.231565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-16T11:45:19.272171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 471
93.1%
1 35
 
6.9%

Most occurring characters

ValueCountFrequency (%)
0 471
93.1%
1 35
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 506
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 471
93.1%
1 35
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 506
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 471
93.1%
1 35
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 506
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 471
93.1%
1 35
 
6.9%

NOX
Real number (ℝ)

High correlation 

Distinct81
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.55469506
Minimum0.385
Maximum0.871
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-07-16T11:45:19.333569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.385
5-th percentile0.40925
Q10.449
median0.538
Q30.624
95-th percentile0.74
Maximum0.871
Range0.486
Interquartile range (IQR)0.175

Descriptive statistics

Standard deviation0.11587768
Coefficient of variation (CV)0.20890339
Kurtosis-0.064667133
Mean0.55469506
Median Absolute Deviation (MAD)0.0875
Skewness0.72930792
Sum280.6757
Variance0.013427636
MonotonicityNot monotonic
2025-07-16T11:45:19.415935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.538 23
 
4.5%
0.713 18
 
3.6%
0.437 17
 
3.4%
0.871 16
 
3.2%
0.624 15
 
3.0%
0.489 15
 
3.0%
0.693 14
 
2.8%
0.605 14
 
2.8%
0.74 13
 
2.6%
0.544 12
 
2.4%
Other values (71) 349
69.0%
ValueCountFrequency (%)
0.385 1
 
0.2%
0.389 1
 
0.2%
0.392 2
0.4%
0.394 1
 
0.2%
0.398 2
0.4%
0.4 4
0.8%
0.401 3
0.6%
0.403 3
0.6%
0.404 3
0.6%
0.405 3
0.6%
ValueCountFrequency (%)
0.871 16
3.2%
0.77 8
1.6%
0.74 13
2.6%
0.718 6
 
1.2%
0.713 18
3.6%
0.7 11
2.2%
0.693 14
2.8%
0.679 8
1.6%
0.671 7
 
1.4%
0.668 3
 
0.6%

RM
Real number (ℝ)

High correlation 

Distinct446
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2846344
Minimum3.561
Maximum8.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-07-16T11:45:19.488312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3.561
5-th percentile5.314
Q15.8855
median6.2085
Q36.6235
95-th percentile7.5875
Maximum8.78
Range5.219
Interquartile range (IQR)0.738

Descriptive statistics

Standard deviation0.70261714
Coefficient of variation (CV)0.11179921
Kurtosis1.8915004
Mean6.2846344
Median Absolute Deviation (MAD)0.3455
Skewness0.40361213
Sum3180.025
Variance0.49367085
MonotonicityNot monotonic
2025-07-16T11:45:19.582503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.229 3
 
0.6%
6.127 3
 
0.6%
5.713 3
 
0.6%
6.417 3
 
0.6%
6.405 3
 
0.6%
6.167 3
 
0.6%
5.304 2
 
0.4%
5.39 2
 
0.4%
6.193 2
 
0.4%
4.138 2
 
0.4%
Other values (436) 480
94.9%
ValueCountFrequency (%)
3.561 1
0.2%
3.863 1
0.2%
4.138 2
0.4%
4.368 1
0.2%
4.519 1
0.2%
4.628 1
0.2%
4.652 1
0.2%
4.88 1
0.2%
4.903 1
0.2%
4.906 1
0.2%
ValueCountFrequency (%)
8.78 1
0.2%
8.725 1
0.2%
8.704 1
0.2%
8.398 1
0.2%
8.375 1
0.2%
8.337 1
0.2%
8.297 1
0.2%
8.266 1
0.2%
8.259 1
0.2%
8.247 1
0.2%

AGE
Real number (ℝ)

High correlation 

Distinct356
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.574901
Minimum2.9
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-07-16T11:45:19.654649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile17.725
Q145.025
median77.5
Q394.075
95-th percentile100
Maximum100
Range97.1
Interquartile range (IQR)49.05

Descriptive statistics

Standard deviation28.148861
Coefficient of variation (CV)0.41048344
Kurtosis-0.96771559
Mean68.574901
Median Absolute Deviation (MAD)19.55
Skewness-0.59896264
Sum34698.9
Variance792.3584
MonotonicityNot monotonic
2025-07-16T11:45:19.743968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 43
 
8.5%
87.9 4
 
0.8%
97.9 4
 
0.8%
98.8 4
 
0.8%
96 4
 
0.8%
98.2 4
 
0.8%
95.4 4
 
0.8%
96.2 3
 
0.6%
21.4 3
 
0.6%
88 3
 
0.6%
Other values (346) 430
85.0%
ValueCountFrequency (%)
2.9 1
0.2%
6 1
0.2%
6.2 1
0.2%
6.5 1
0.2%
6.6 2
0.4%
6.8 1
0.2%
7.8 2
0.4%
8.4 1
0.2%
8.9 1
0.2%
9.8 1
0.2%
ValueCountFrequency (%)
100 43
8.5%
99.3 1
 
0.2%
99.1 1
 
0.2%
98.9 3
 
0.6%
98.8 4
 
0.8%
98.7 1
 
0.2%
98.5 1
 
0.2%
98.4 2
 
0.4%
98.3 2
 
0.4%
98.2 4
 
0.8%

DIS
Real number (ℝ)

High correlation 

Distinct412
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7950427
Minimum1.1296
Maximum12.1265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-07-16T11:45:19.842447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.1296
5-th percentile1.461975
Q12.100175
median3.20745
Q35.188425
95-th percentile7.8278
Maximum12.1265
Range10.9969
Interquartile range (IQR)3.08825

Descriptive statistics

Standard deviation2.1057101
Coefficient of variation (CV)0.55485809
Kurtosis0.48794112
Mean3.7950427
Median Absolute Deviation (MAD)1.29115
Skewness1.0117806
Sum1920.2916
Variance4.4340151
MonotonicityNot monotonic
2025-07-16T11:45:19.933680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.4952 5
 
1.0%
5.4007 4
 
0.8%
5.2873 4
 
0.8%
5.7209 4
 
0.8%
6.8147 4
 
0.8%
6.0622 3
 
0.6%
6.4798 3
 
0.6%
3.6519 3
 
0.6%
7.309 3
 
0.6%
6.498 3
 
0.6%
Other values (402) 470
92.9%
ValueCountFrequency (%)
1.1296 1
0.2%
1.137 1
0.2%
1.1691 1
0.2%
1.1742 1
0.2%
1.1781 1
0.2%
1.2024 1
0.2%
1.2852 1
0.2%
1.3163 1
0.2%
1.3216 1
0.2%
1.3325 1
0.2%
ValueCountFrequency (%)
12.1265 1
0.2%
10.7103 2
0.4%
10.5857 2
0.4%
9.2229 1
0.2%
9.2203 2
0.4%
9.1876 1
0.2%
9.0892 1
0.2%
8.9067 2
0.4%
8.7921 2
0.4%
8.6966 1
0.2%

RAD
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5494071
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-07-16T11:45:20.006332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q324
95-th percentile24
Maximum24
Range23
Interquartile range (IQR)20

Descriptive statistics

Standard deviation8.7072594
Coefficient of variation (CV)0.91181152
Kurtosis-0.86723199
Mean9.5494071
Median Absolute Deviation (MAD)2
Skewness1.0048146
Sum4832
Variance75.816366
MonotonicityNot monotonic
2025-07-16T11:45:20.076349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
24 132
26.1%
5 115
22.7%
4 110
21.7%
3 38
 
7.5%
6 26
 
5.1%
8 24
 
4.7%
2 24
 
4.7%
1 20
 
4.0%
7 17
 
3.4%
ValueCountFrequency (%)
1 20
 
4.0%
2 24
 
4.7%
3 38
 
7.5%
4 110
21.7%
5 115
22.7%
6 26
 
5.1%
7 17
 
3.4%
8 24
 
4.7%
24 132
26.1%
ValueCountFrequency (%)
24 132
26.1%
8 24
 
4.7%
7 17
 
3.4%
6 26
 
5.1%
5 115
22.7%
4 110
21.7%
3 38
 
7.5%
2 24
 
4.7%
1 20
 
4.0%

TAX
Real number (ℝ)

High correlation 

Distinct66
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean408.23715
Minimum187
Maximum711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-07-16T11:45:20.164243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum187
5-th percentile222
Q1279
median330
Q3666
95-th percentile666
Maximum711
Range524
Interquartile range (IQR)387

Descriptive statistics

Standard deviation168.53712
Coefficient of variation (CV)0.4128412
Kurtosis-1.142408
Mean408.23715
Median Absolute Deviation (MAD)73
Skewness0.66995594
Sum206568
Variance28404.759
MonotonicityNot monotonic
2025-07-16T11:45:20.370528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
666 132
26.1%
307 40
 
7.9%
403 30
 
5.9%
437 15
 
3.0%
304 14
 
2.8%
264 12
 
2.4%
398 12
 
2.4%
384 11
 
2.2%
277 11
 
2.2%
330 10
 
2.0%
Other values (56) 219
43.3%
ValueCountFrequency (%)
187 1
 
0.2%
188 7
1.4%
193 8
1.6%
198 1
 
0.2%
216 5
1.0%
222 7
1.4%
223 5
1.0%
224 10
2.0%
226 1
 
0.2%
233 9
1.8%
ValueCountFrequency (%)
711 5
 
1.0%
666 132
26.1%
469 1
 
0.2%
437 15
 
3.0%
432 9
 
1.8%
430 3
 
0.6%
422 1
 
0.2%
411 2
 
0.4%
403 30
 
5.9%
402 2
 
0.4%

PTRATIO
Real number (ℝ)

High correlation 

Distinct46
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.455534
Minimum12.6
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-07-16T11:45:20.487447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum12.6
5-th percentile14.7
Q117.4
median19.05
Q320.2
95-th percentile21
Maximum22
Range9.4
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.1649455
Coefficient of variation (CV)0.11730604
Kurtosis-0.28509138
Mean18.455534
Median Absolute Deviation (MAD)1.15
Skewness-0.80232493
Sum9338.5
Variance4.6869891
MonotonicityNot monotonic
2025-07-16T11:45:20.553560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
20.2 140
27.7%
14.7 34
 
6.7%
21 27
 
5.3%
17.8 23
 
4.5%
19.2 19
 
3.8%
17.4 18
 
3.6%
19.1 17
 
3.4%
18.6 17
 
3.4%
18.4 16
 
3.2%
16.6 16
 
3.2%
Other values (36) 179
35.4%
ValueCountFrequency (%)
12.6 3
 
0.6%
13 12
 
2.4%
13.6 1
 
0.2%
14.4 1
 
0.2%
14.7 34
6.7%
14.8 3
 
0.6%
14.9 4
 
0.8%
15.1 1
 
0.2%
15.2 13
 
2.6%
15.3 3
 
0.6%
ValueCountFrequency (%)
22 2
 
0.4%
21.2 15
 
3.0%
21.1 1
 
0.2%
21 27
 
5.3%
20.9 11
 
2.2%
20.2 140
27.7%
20.1 5
 
1.0%
19.7 8
 
1.6%
19.6 8
 
1.6%
19.2 19
 
3.8%

B
Real number (ℝ)

Distinct357
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean356.67403
Minimum0.32
Maximum396.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-07-16T11:45:20.637058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.32
5-th percentile84.59
Q1375.3775
median391.44
Q3396.225
95-th percentile396.9
Maximum396.9
Range396.58
Interquartile range (IQR)20.8475

Descriptive statistics

Standard deviation91.294864
Coefficient of variation (CV)0.25596162
Kurtosis7.2268175
Mean356.67403
Median Absolute Deviation (MAD)5.46
Skewness-2.8903737
Sum180477.06
Variance8334.7523
MonotonicityNot monotonic
2025-07-16T11:45:20.720332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
396.9 121
 
23.9%
395.24 3
 
0.6%
393.74 3
 
0.6%
395.58 2
 
0.4%
393.23 2
 
0.4%
376.14 2
 
0.4%
389.71 2
 
0.4%
395.56 2
 
0.4%
395.6 2
 
0.4%
390.94 2
 
0.4%
Other values (347) 365
72.1%
ValueCountFrequency (%)
0.32 1
0.2%
2.52 1
0.2%
2.6 1
0.2%
3.5 1
0.2%
3.65 1
0.2%
6.68 1
0.2%
7.68 1
0.2%
9.32 1
0.2%
10.48 1
0.2%
16.45 1
0.2%
ValueCountFrequency (%)
396.9 121
23.9%
396.42 1
 
0.2%
396.33 1
 
0.2%
396.3 1
 
0.2%
396.28 1
 
0.2%
396.24 1
 
0.2%
396.23 1
 
0.2%
396.21 2
 
0.4%
396.14 1
 
0.2%
396.06 2
 
0.4%

LSTAT
Real number (ℝ)

High correlation 

Distinct455
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.653063
Minimum1.73
Maximum37.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-07-16T11:45:20.803321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.73
5-th percentile3.7075
Q16.95
median11.36
Q316.955
95-th percentile26.8075
Maximum37.97
Range36.24
Interquartile range (IQR)10.005

Descriptive statistics

Standard deviation7.1410615
Coefficient of variation (CV)0.56437413
Kurtosis0.49323952
Mean12.653063
Median Absolute Deviation (MAD)4.795
Skewness0.90646009
Sum6402.45
Variance50.99476
MonotonicityNot monotonic
2025-07-16T11:45:20.894755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.36 3
 
0.6%
8.05 3
 
0.6%
14.1 3
 
0.6%
7.79 3
 
0.6%
18.13 3
 
0.6%
8.1 2
 
0.4%
10.45 2
 
0.4%
6.72 2
 
0.4%
3.11 2
 
0.4%
5.68 2
 
0.4%
Other values (445) 481
95.1%
ValueCountFrequency (%)
1.73 1
0.2%
1.92 1
0.2%
1.98 1
0.2%
2.47 1
0.2%
2.87 1
0.2%
2.88 1
0.2%
2.94 1
0.2%
2.96 1
0.2%
2.97 1
0.2%
2.98 1
0.2%
ValueCountFrequency (%)
37.97 1
0.2%
36.98 1
0.2%
34.77 1
0.2%
34.41 1
0.2%
34.37 1
0.2%
34.02 1
0.2%
31.99 1
0.2%
30.81 2
0.4%
30.63 1
0.2%
30.62 1
0.2%

PRICE
Real number (ℝ)

High correlation 

Distinct229
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.532806
Minimum5
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-07-16T11:45:20.975893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10.2
Q117.025
median21.2
Q325
95-th percentile43.4
Maximum50
Range45
Interquartile range (IQR)7.975

Descriptive statistics

Standard deviation9.1971041
Coefficient of variation (CV)0.40816505
Kurtosis1.4951969
Mean22.532806
Median Absolute Deviation (MAD)4
Skewness1.1080984
Sum11401.6
Variance84.586724
MonotonicityNot monotonic
2025-07-16T11:45:21.053249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 16
 
3.2%
25 8
 
1.6%
23.1 7
 
1.4%
22 7
 
1.4%
21.7 7
 
1.4%
20.6 6
 
1.2%
19.4 6
 
1.2%
22.6 5
 
1.0%
21.4 5
 
1.0%
21.2 5
 
1.0%
Other values (219) 434
85.8%
ValueCountFrequency (%)
5 2
0.4%
5.6 1
 
0.2%
6.3 1
 
0.2%
7 2
0.4%
7.2 3
0.6%
7.4 1
 
0.2%
7.5 1
 
0.2%
8.1 1
 
0.2%
8.3 2
0.4%
8.4 2
0.4%
ValueCountFrequency (%)
50 16
3.2%
48.8 1
 
0.2%
48.5 1
 
0.2%
48.3 1
 
0.2%
46.7 1
 
0.2%
46 1
 
0.2%
45.4 1
 
0.2%
44.8 1
 
0.2%
44 1
 
0.2%
43.8 1
 
0.2%

Interactions

2025-07-16T11:45:17.474418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:44:52.929458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:44:56.228968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:44:58.994803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:01.771585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:04.910729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:07.999169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:11.110864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:12.560632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:13.599544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:14.549551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:15.489866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:16.536943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:17.547703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:44:53.128253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:44:56.399922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:44:59.240695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:01.945850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-16T11:44:54.014873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:44:57.228782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:44:59.985577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-16T11:45:17.962025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-16T11:45:06.744896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:09.873741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:12.195023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-16T11:44:58.340403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:01.053809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:03.986160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:07.237445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:10.294675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:12.329288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:13.376346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:14.328482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:15.260151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:16.307300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:17.246994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:18.237692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:44:55.707638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:44:58.540761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:01.253709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:04.479998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:07.537303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:10.546743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:12.411582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:13.451597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:14.398150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:15.343546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:16.402453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:17.340623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:18.315535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:44:55.940382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:44:58.745551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:01.471877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:04.703019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:07.791802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:10.853535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:12.495195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:13.524202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:14.483352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:15.415644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:16.468904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-16T11:45:17.411499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-16T11:45:21.129916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AGEBCHASCRIMDISINDUSLSTATNOXPRICEPTRATIORADRMTAXZN
AGE1.000-0.2280.0000.704-0.8020.6790.6570.795-0.5480.3550.418-0.2780.526-0.544
B-0.2281.0000.052-0.3610.250-0.286-0.211-0.2970.186-0.072-0.2830.054-0.3300.163
CHAS0.0000.0521.0000.0000.0840.1460.0000.1750.2100.1570.1310.0120.0430.024
CRIM0.704-0.3610.0001.000-0.7450.7360.6350.821-0.5590.4650.728-0.3090.729-0.572
DIS-0.8020.2500.084-0.7451.000-0.757-0.564-0.8800.446-0.322-0.4960.263-0.5740.615
INDUS0.679-0.2860.1460.736-0.7571.0000.6390.791-0.5780.4340.456-0.4150.664-0.643
LSTAT0.657-0.2110.0000.635-0.5640.6391.0000.637-0.8530.4670.394-0.6410.534-0.490
NOX0.795-0.2970.1750.821-0.8800.7910.6371.000-0.5630.3910.586-0.3100.650-0.635
PRICE-0.5480.1860.210-0.5590.446-0.578-0.853-0.5631.000-0.556-0.3470.634-0.5620.438
PTRATIO0.355-0.0720.1570.465-0.3220.4340.4670.391-0.5561.0000.318-0.3130.453-0.448
RAD0.418-0.2830.1310.728-0.4960.4560.3940.586-0.3470.3181.000-0.1070.705-0.279
RM-0.2780.0540.012-0.3090.263-0.415-0.641-0.3100.634-0.313-0.1071.000-0.2720.361
TAX0.526-0.3300.0430.729-0.5740.6640.5340.650-0.5620.4530.705-0.2721.000-0.371
ZN-0.5440.1630.024-0.5720.615-0.643-0.490-0.6350.438-0.448-0.2790.361-0.3711.000

Missing values

2025-07-16T11:45:18.522724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-16T11:45:18.631794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATPRICE
00.0063218.02.3100.5386.57565.24.0900129615.3396.904.9824.0
10.027310.07.0700.4696.42178.94.9671224217.8396.909.1421.6
20.027290.07.0700.4697.18561.14.9671224217.8392.834.0334.7
30.032370.02.1800.4586.99845.86.0622322218.7394.632.9433.4
40.069050.02.1800.4587.14754.26.0622322218.7396.905.3336.2
50.029850.02.1800.4586.43058.76.0622322218.7394.125.2128.7
60.0882912.57.8700.5246.01266.65.5605531115.2395.6012.4322.9
70.1445512.57.8700.5246.17296.15.9505531115.2396.9019.1527.1
80.2112412.57.8700.5245.631100.06.0821531115.2386.6329.9316.5
90.1700412.57.8700.5246.00485.96.5921531115.2386.7117.1018.9
CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATPRICE
4960.289600.09.6900.5855.39072.92.7986639119.2396.9021.1419.7
4970.268380.09.6900.5855.79470.62.8927639119.2396.9014.1018.3
4980.239120.09.6900.5856.01965.32.4091639119.2396.9012.9221.2
4990.177830.09.6900.5855.56973.52.3999639119.2395.7715.1017.5
5000.224380.09.6900.5856.02779.72.4982639119.2396.9014.3316.8
5010.062630.011.9300.5736.59369.12.4786127321.0391.999.6722.4
5020.045270.011.9300.5736.12076.72.2875127321.0396.909.0820.6
5030.060760.011.9300.5736.97691.02.1675127321.0396.905.6423.9
5040.109590.011.9300.5736.79489.32.3889127321.0393.456.4822.0
5050.047410.011.9300.5736.03080.82.5050127321.0396.907.8811.9